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Writer's pictureManiKumar Jami

Designing an Intelligent Pricing Engine for Refurbished Two-Wheelers


Objective:


To design and implement a pricing engine that accurately determines the price of refurbished second-hand two-wheelers based on multiple dynamic factors.



Business Need


In the competitive world of second-hand vehicle marketplaces, determining the right price for each vehicle is crucial. Traditional pricing methods often rely on manual assessments, leading to inconsistencies, inefficiencies, and potential revenue loss. The need for an automated pricing engine arises from the following business requirements:


  1. Consistency: Ensuring uniform pricing across all vehicles, reducing human error and subjectivity.

  2. Efficiency: Streamlining the pricing process to handle a large volume of vehicles quickly and accurately.

  3. Competitiveness: Adjusting prices dynamically based on real-time market data to stay competitive.

  4. Transparency: Providing clear and justifiable pricing to customers, building trust and credibility.

  5. Optimization: Maximizing profitability by considering various cost factors and market conditions.




Architecture Overview



  1. Data Collection Layer:

  • Source Data:

  • Procurement Data: Information on vehicles procured from individual customers.


Refurbishment Data: Costs and details of refurbishment activities.




  • Market Data: Current market prices, demand trends, competitor inventory, and search trends.



  • Website Data: Traffic data and search results for specific models on the website.

  • Tools and Technologies:

    • Data Scraping Tools: For collecting competitor pricing and market trends.

  • APIs: For accessing market price data and demand trends.

  • Databases: SQL/NoSQL databases to store raw and processed data.





  • Data Processing Layer:

  • ETL (Extract, Transform, Load):

  • Extract: Pulling data from various sources.

  • Transform: Cleaning, normalizing, and enriching the data.

  • Load: Storing the processed data into a central data warehouse.

Vehicle ID

Make

Model

Year

Condition

Procurement Cost (₹)

Color

Mileage (km)

Labor Cost (₹)

Parts Cost (₹)

Total Refurbishment Cost (₹)

Average Market Price (₹)

Competitor Inventory

Demand Trend (Score)

Search Volume

Page Views

V001

Honda

Activa

2018

Good

35,000

Red

20,000

2,000

4,000

6,000

42,000

High

7

1500

2000

V002

Yamaha

FZ

2016

Fair

45,000

Blue

35,000

3,000

5,500

8,500

55,000

Medium

5

1200

1800

V003

Royal Enfield

Classic 350

2019

Excellent

90,000

Black

10,000

1,500

2,000

3,500

1,00,000

Low

9

800

1500

Transformed Data (after cleaning and normalization):

Vehicle ID

Make

Model

Age (Years)

Condition Score

Procurement Cost (₹)

Refurbishment Cost

Market Price (₹)

Competitor Inventory Score

Demand Score

Search Volume Score

Page Views Score

V001

Honda

Activa

6

7

35,000

6,000

42,000

3

7

6

7

V002

Yamaha

FZ

8

5

45,000

8,500

55,000

2

5

5

6

V003

Royal Enfield

Classic 350

5

9

90,000

3,500

1,00,000

1

9

4

5


  • Tools and Technologies:

  • Apache Kafka: For real-time data streaming.

  • Apache Spark: For large-scale data processing.

  • AWS Redshift/BigQuery: For data warehousing.

  • Data Analysis Layer:

  • Machine Learning Models:

  • Regression Models: To predict prices based on historical data.

  • Classification Models: To categorize vehicles based on various attributes.

  • Time Series Models: To predict market trends and demand.

  • Linear Regression:

  • Used to predict the final price of vehicles.

  • The model learns the relationship between input features (procurement cost, refurbishment cost, age, etc.) and the target variable (final price).

  • Evaluation metrics (MAE, MSE, R-squared) help assess the model's performance.



  • Decision Tree Classifier:

  • Categorizes vehicles into segments like budget, mid-range, and premium.

  • The model uses features like age, condition score, and market price to classify each vehicle.

  • Accuracy score measures how well the model performs.



  • ARIMA:

  • Forecasts future market prices based on historical data.

  • The model captures patterns in the time series data (e.g., monthly market prices).

  • The forecasted values help in understanding future price trends.



  • Tools and Technologies:

  • Python/R: For developing machine learning models.

  • TensorFlow/PyTorch: For deep learning models.

  • Jupyter Notebooks: For exploratory data analysis and prototyping.

  • Pricing Engine Layer:

  • Algorithm:

  • Input Parameters: Procurement cost, refurbishment cost, age, model, color, current market price, demand, competitor inventory, website traffic, etc.

  • Weight Assignment: Assigning weights to each factor based on their importance.

  • Price Calculation: Using a combination of rule-based and machine learning models to calculate the final price.



Price Calculation Inputs:

  • V001:

  • Procurement Cost: ₹35,000

  • Refurbishment Cost: ₹6,000

  • Age: 6 years

  • Condition Score: 7

  • Market Price: ₹42,000

  • Competitor Inventory Score: 3

  • Demand Score: 7

  • Search Volume Score: 6

  • Page Views Score: 7



Weight Assignment Example:

  • Procurement Cost: 0.3

  • Refurbishment Cost: 0.2

  • Age: 0.1

  • Condition Score: 0.15

  • Market Price: 0.1

  • Competitor Inventory Score: 0.05

  • Demand Score: 0.05

  • Search Volume Score: 0.025

  • Page Views Score: 0.025


Final Price (₹) = (0.3 * 35,000) + (0.2 * 6,000) + (0.1 * 6) + (0.15 * 7) + (0.1 * 42,000) + (0.05 * 3) + (0.05 * 7) + (0.025 * 6) + (0.025 * 7)

Final Price (₹) ≈ 41,000



Vehicle ID

Final Price (₹)

V001

41,000

V002

53,000

V003

97,000



  • Tools and Technologies:

  • Flask/Django: For building the API to serve the pricing engine.

  • Docker/Kubernetes: For containerization and orchestration.

  • User Interface Layer:

  • Admin Dashboard:

  • Features: View and adjust pricing factors, monitor real-time price adjustments, and generate reports.

  • Customer Interface:

  • Features: Display final prices on the website, allow users to see price breakdown.

  • Tools and Technologies:

  • React/Vue.js: For building the frontend.

  • RESTful APIs: For communication between frontend and backend.






Metrics To Track:





Challenges :


1. Data Integration and Migration


Challenge: Moving Data from Excel Sheets

  • Initially, data may be stored in disparate Excel sheets, making integration into a central system challenging.

  • Solution: Implement an ETL (Extract, Transform, Load) process to automate data extraction from Excel sheets, transform it into a standardized format, and load it into a central database. Tools like Apache Nifi or Talend can help streamline this process.



Frequently Updating the Data

Challenge: Keeping Data Up-to-Date

  • The dynamic nature of the market requires frequent updates to procurement costs, refurbishment costs, market prices, and demand trends.

  • Solution: Implement a real-time data pipeline using Apache Kafka to stream data updates continuously. Schedule regular data pulls from external sources using APIs.



User Adoption and Training

Challenge: Ensuring Adoption by Users

  • Users (internal) need to trust and effectively use the new pricing engine.

  • Solution: Provide comprehensive training and support, create detailed documentation, and gather user feedback to improve the system continuously.


 Integration with Existing Systems

Challenge: Seamless Integration with Current Systems



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